Abstract
Promotion is one of the key ingredients in marketing. It is often desirable to find merit in an object (e.g., product, person, organization, or service) and promote it in an appropriate community. In this paper, we propose a novel functionality, called promotion analysis through ranking, for promoting a given object by leveraging highly ranked results. Since the object may not be highly ranked in the global space, our goal is to discover promotive subspaces in which the object becomes prominent. To achieve this goal, the notion of promotiveness is formulated. We show that this functionality is practical and useful in a wide variety of applications such as business intelligence. However, computing promotive subspaces is challenging due to the explosion of search space and high aggregation cost. For efficient computation, we propose a PromoRank framework, and develop three efficient optimization techniques, namely subspace pruning, object pruning, and promotion cube, which are seamlessly integrated into the framework. Our empirical evaluation on two real data sets confirms the effectiveness of promotion analysis, and that our proposed algorithms significantly outperform baseline solutions.
- DBLP. http://www.informatik.uni-trier.de/~ley/db/.Google Scholar
- NBA data set, http://www.basketballreference.com.Google Scholar
- TPC-H. http://www.tpc.org/tpch/.Google Scholar
- N. Bansal, S. Guha, and N. Koudas. Ad-hoc aggregations of ranked lists in the presence of hierarchies. In SIGMOD, pages 67--78, 2008. Google ScholarDigital Library
- K. S. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. In SIGMOD, pages 359--370, 1999. Google ScholarDigital Library
- C. Borgs, J. T. Chayes, N. Immorlica, K. Jain, O. Etesami, and M. Mahdian. Dynamics of bid optimization in online advertisement auctions. In WWW, pages 531--540, 2007. Google ScholarDigital Library
- S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum. Probabilistic ranking of database query results. In VLDB, pages 888--899, 2004. Google ScholarDigital Library
- G. Das, V. Hristidis, N. Kapoor, and S. Sudarshan. Ordering the attributes of query results. In SIGMOD, pages 395--406, 2006. Google ScholarDigital Library
- R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In PODS, 2001. Google ScholarDigital Library
- J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov., 1(1):29--53, 1997. Google ScholarDigital Library
- I. F. Ilyas, G. Beskales, and M. A. Soliman. A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv., 40(4), 2008. Google ScholarDigital Library
- J. M. Kleinberg, C. H. Papadimitriou, and P. Raghavan. A microeconomic view of data mining. Data Min. Knowl. Discov., 2(4):311--324, 1998. Google ScholarDigital Library
- P. Kotler and K. Keller. Marketing Management. Prentice Hall, March 2008.Google Scholar
- R. Kuehl. Design of Experiments: Statistical Principles of Research Design and Analysis. Duxbury Press, 2000.Google Scholar
- C. Li, B. C. Ooi, A. K. H. Tung, and S. Wang. Dada: a data cube for dominant relationship analysis. In SIGMOD, pages 659--670, 2006. Google ScholarDigital Library
- M. Miah, G. Das, V. Hristidis, and H. Mannila. Standing out in a crowd: Selecting attributes for maximum visibility. In ICDE, pages 356--365, 2008. Google ScholarDigital Library
- Z. Shao, J. Han, and D. Xin. Mm-cubing: Computing iceberg cubes by factorizing the lattice space. In SSDBM, pages 213--222, 2004. Google ScholarDigital Library
- J. S. Vitter and M. Wang. Approximate computation of multidimensional aggregates of sparse data using wavelets. In SIGMOD, pages 193--204, 1999. Google ScholarDigital Library
- T. Wu, D. Xin, and J. Han. Arcube: supporting ranking aggregate queries in partially materialized data cubes. In SIGMOD Conference, pages 79--92, 2008. Google ScholarDigital Library
Index Terms
- Promotion analysis in multi-dimensional space
Recommendations
Note. Optimal Promotion Strategies: A Demand-Sided Characterization
We generalize Narasimhan's Narasimhan, Chakravarthi. 1988. Competitive promotional strategies. J. Bus.614 427-449. model of retail promotion to include multiple products and general demand functions. Doing so allows us to further characterize optimal ...
Findings---Retailer Promotion Pass-Through: A Measure, Its Magnitude, and Its Determinants
We use data on all manufacturer funding and promotion activity by a major U.S. retailer during a two-year period to compute promotion pass-through and assess its magnitude. Then, we estimate a two-tiered probit and lognormal regression model to study ...
Comments